Data science and computer science represent two appealing and promising career paths for those looking to get into technology. Both fields present opportunities to take on interesting challenges and make an impact through meaningful work.
When deciding between specializing in data science versus computer science, aspirants ponder – which discipline offers better prospects? This article provides an extensive comparison of both fields across key aspects to help prospective students and professionals choose between these exciting directions.
By evaluating different parameters such as required competencies, day-to-day responsibilities, earning potential and scope for evolution across data science and computer science jobs, readers can arrive at a well-informed decision.
What is Data Science?
Data science is an interdisciplinary field focused on extracting insights from data. Data scientists apply statistics, programming, analytics, and machine learning to make discoveries and predictions from large, complex data sets.
Key characteristics of data science are as under:
- Collecting, cleaning and organizing data from various sources
- Using programming languages like Python and R to analyze and model data
- Applying statistical and machine learning techniques to extract meaning from data
- Developing algorithms and predictive models to identify trends and patterns
- Data visualization using tools like Tableau to communicate insights
Data scientists work across nearly every industry. Tech firms like Google, Facebook and Microsoft employ data scientists to optimize products, ad targeting and user experiences. Banks and financial institutions use data science to detect fraud, analyze risk, and make investment decisions. Even fields like healthcare, retail, sports rely on data science now.
It’s an extremely promising field – LinkedIn’s 2020 Emerging Jobs report named Data Science as the top emerging job for 5 years running. The average data scientist salary is also very lucrative at over $117,000 in the US according to Glassdoor.
What is Computer Science?
Computer Science is the study of computers and computational systems. Computer scientists focus primarily on software and software systems including their theory, design, development and application.
Key aspects of computer science are:
- Designing and optimizing computer hardware and software
- Creating advanced computer programs and coding languages
- Ensuring the security, privacy, correctness and efficiency of systems
- Using mathematics and logic to process information and solve computational problems
- Cloud computing, cryptography, databases and data compression
- Studying artificial intelligence (AI) models like machine learning, neural networks, robotics and more.
There is huge demand for qualified software engineers and developers across industries as companies rely more on technology and automation. The average base salary for computer science graduates exceeds $102,000 in the US as per Glassdoor.
Career paths include software engineering, web or mobile app development, computational theory, cybersecurity, machine learning engineering, and beyond. Most large tech firms like Apple, Amazon, Google as well as major enterprises hire computer science grads.
Data Science vs Computer Science
Though data science and computer science have some overlap, they ask different questions and serve complementary purposes. Think of data science as focused on analyzing and extracting meaning from existing data while computer science is focused on creating and optimizing the systems for processing data.
Here’s a head-to-head comparison table:
|Extracting actionable insights from data through analytics, modeling and visualization
|Creating computational systems and software, designing efficient algorithms
|Statistics, Machine Learning, Analytics, Math, Data Visualization
|Computer Programming, Software Engineering, Computational Theory
|Sample job roles
|Data Scientist, Data Analyst, Business Analyst, BI Developer, ML Engineer
|Software Engineer, App Developer, Systems Architect, Programmer, Computational Researcher
|Sample job tasks
|Collecting, cleaning and converting raw data into usable format, identifying trends and patterns in data through modeling to drive business solutions, reporting insights using statistical graphics and data visualization
|Designing, developing and testing software applications across domains, building computer programs and coding languages, using mathematics to solve engineering problems
|Python, R, SQL, Tableau, Excel
|Nearly every industry leverages data science from technology, healthcare, ecommerce, finance, transportation and more
|All software companies, big tech firms like Google/Amazon/Facebook, finance firms, video game studios, engineering organizations
In short, data science can be summed up as:
- Using computer science skills to extract value from data
- Applying advanced analytics, AI and machine learning on real-world problems
- Enabling data-driven decision making through statistical analysis and translation
While computer science is broader and focuses on:
- Applying principles of engineering, mathematics & science to study computation, data processing and information systems
- Designing, developing and optimizing the software and hardware that power the technology we use
- Creating solutions to computation, automation and AI problems across different applications
The two fields work hand-in-hand – with data science leveraging tools and infrastructure created by computer scientists to derive insights at scale for problems across verticals.
Which Should You Choose? Data Science vs Computer Science
So if you need to decide between focusing your studies and career on data science vs computer science, which should you pick?
Here are a few key considerations:
Interests & Personality Traits
- Data Science suits people interested in quantitative analysis, mathematical modeling, statistics and translating data findings across teams and leadership.
- Computer Science suits those interested in building and programming efficient, scalable software systems and applications from scratch.
- Data Science needs stat CS skills + math/stats know-how + communication abilities
- Computer Science needs software engineering skills + computational thinking abilities
- In Data Science – Advance from data analyst to scientist, then to manager/lead roles
- In Computer Science – Progress across engineering levels from associate to architect
- Data Scientists earn lucrative salaries especially with some experience – average total comp exceeding $150K per year
- Software engineering roles also pay very well with average salaries comfortably over 6 figures
Flexibility & Domain Focus
- Data science enables flexibility to transition across domains and leverage transferable analytics skills
- Computer science skills are broadly applicable but you tend to specialize around particular systems/tools
So evaluate your interests, skills and career aspirations. If statistical analysis excites you, data science is a great option. If you prefer building over analyzing, computer science is the way to go. Many professionals also choose to gain skills across both areas to remain versatile and multi-dimensional in their capabilities.
Combining Data Science and Computer Science
Though data science and computer science demand different skill sets and duties, combining proficiency in both areas can prove extremely valuable in driving end-to-end solutions.
Here are some of the top ways data science works with computer science:
- Software Engineers in Data Science – Software engineers are needed to build platforms and infrastructure to manage data pipelines, orchestrate large-scale data processing, create frameworks for machine learning model deployment and guide tool development.
- Data-driven Software Solutions – Expert software developers are needed to create custom programs, apps and visualization dashboards based on the outputs from data science and analytics to deliver value to business stakeholders.
- Platform Engineering – Platform engineers work at the intersection of data science and engineering – responsible for building reliable data pipelines, scalable infra and process automation to enable faster model development.
- Machine Learning Engineering – ML engineers liaise between data scientists building models and software engineers translating models to production systems effectively.
- Quantitative Analysts in Finance – In investment banks, quant developers bring together software expertise with complex data modeling and econometrics to drive trading, forecasting and investment insights.
Which Path Should You Pick?
Determining whether to specialize in data science vs computer science depends primarily on your interests, talents, profile and career aspirations.
As an aspiring technologist interested in a promising career combining intellectual challenge, great salaries with lots of development potential – both data science and computer science represent excellent options.
Evaluate whether you lean more towards analytics and statistics or systems and software. Envision the day-day tasks each role undertakes and capabilities needed. Do you see yourself extracting insights, telling stories with data and enabling decisions or building, optimizing and coding complex programs that drive technological innovation?
Once you have more clarity, excel at gaining core skills in either discipline through a mix of formal education and hands-on learning. While specializing, also consider brushing up secondary knowledge across the other field to boost versatility.
And remember data science vs computer science does not have to be an either-or choice! Combine strengths across both fields to become a well-rounded, highly impactful industry leader building the future.